Senior Machine Learning Engineer, Scenes, Traffic Controls

Waymo; Mountain View (CA); On-site; USD 213,000 - 263,000; Full time

Job summary

Waymo LLC is seeking a Machine Learning Engineer in Driver Refinement and Validation. You will own end-to-end ML features for Scene Understanding, curate large driving datasets, and iterate multi-task models to improve calibration and robustness.

You will integrate models into the onboard stack in C++ and collaborate across perception, mapping and ML infrastructure teams. The role requires hands-on production ML experience, strong data mining skills, and a track record of rigorous evaluation and

Qualifications

• Hands-on experience shipping ML models to production.
• Strong data mining / data engineering skills with large-scale pipelines.
• Proficiency in Python and a modern DL framework; production C++.
• Cross-stack ownership across data, model and systems/serving.
• Rigorous evaluation incl. metrics design and A/B or staged rollouts.

Responsibilities

• Own ML features for Scene Understanding end to end: data → model → eval → onboard launch.
• Mine and curate large-scale driving data (real and simulated) for training/eval sets.
• Design, train, and iterate on model tasks and heads in large multi-task models; improve calibration/robustness.
• Build rigorous evaluation: metrics, slicing, side-by-sides; translate results into launch decisions.
• Integrate models into onboard stack (C++) with safe heuristics and map-contradiction handling.
• Triage real driving events to identify root causes and propose fixes, collaborating with multiple teams.

Skills

Production ML deployment

Data mining / data engineering

Python

C++

Cross-stack experience

Model evaluation

Education

BS/MS/PhD in CS, EE, Robotics, ML, or related field

Tools

Beam/Flume/Spark

Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As a Machine Learning Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.

You will

• Own ML features for Scene Understanding (traffic controls, signs, special regions such as RRX and flood zones, lane states) end to end: data → model → eval → onboard launch
• Mine and curate large-scale driving data (real and simulated) to find long-tail failures, build training and eval sets, and design labeling and supervision
• Design, train, and iterate on model tasks and heads in large multi-task models. Improve calibration, robustness, and generalization to new geographies
• Build rigorous evaluation: PRT metrics, slicing taxonomies, side-by-sides, and staging launches, and turn the results into launch decisions
• Integrate models into the onboard stack (C++), combining learned outputs with safe heuristic fallbacks, Mendel-guarded rollouts, and map-contradiction handling
• Triage real driving events, find the root cause anywhere in the stack, and propose surgical or systemic fixes. Work closely with Perception, Reasoning, Mapping, and ML Infrastructure teams

You have

• BS/MS/PhD in CS, EE, Robotics, ML, or a related field, or equivalent practical experience.
• Hands-on experience shipping ML models to production, including training, offline evaluation, and deployment (L4: 2+ yrs; L5: 4+ yrs, or a PhD plus 2+ yrs)
• Strong data mining / data engineering: building large-scale batch pipelines (Beam/Flume/Spark or similar) and analytical SQL to find, slice, and curate data
• Proficiency in Python with a modern DL framework (JAX, TensorFlow, or PyTorch) and production C++
• Demonstrated cross-stack generalist work: has owned problems that crossed data, model, and systems/serving boundaries
• Rigorous evaluation instincts: metric design, error analysis and slicing, and A/B or staged rollout

We prefer

• AV/Robotics experience, especially perception (detection, classification, tracking)
• Experience with transformer or temporal models (GRU/RNN), multi-task learning, and model calibration/uncertainty
• Labeling-pipeline and supervision design; autolabeling or synthetic data
• TPU/accelerator performance work; exporting models for onboard/edge inference
• Builds internal tools (visualization, triage, experiment tracking) that accelerate the team

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Salary Range

$213,000—$263,000 USD